AI Glossary of Terms
The AI technology space is expanding rapidly and with that a whole new vocabulary needs to be learned. I have gathered many of those that I come across on a frequent basis (I actually maintain this as an internal resource in our company as well). For now, I have split into Generic AI terms; Semantic Kernel; Microsoft Copilot; and Advanced Terms.
Please let me know if have missed a term that you feel should be on the list.
Generic AI terms
Large Language Models (LLMs): Advanced AI models capable of understanding and generating human-like text based on the input they receive.
GPT and GAI (Generative Artificial Intelligence): Advanced AI models and techniques used for generating content.
Azure OpenAI Service: The cloud service where the LLMs used for Microsoft 365 Copilot are hosted.
DALL-E: An AI model developed by OpenAI for generating images from textual descriptions.
Prompts: User queries or questions that Microsoft 365 Copilot responds to using the sophisticated map.
Prompt Engineering: The process of designing and optimizing prompts to get desired outputs from AI models.
Vector: A mathematical representation that combines phrases, meanings, relationships, and context of data.
Vector Database: A database designed to store and manage vector data, which can be used in conjunction with embeddings.
Embeddings: Embeddings are a type of word representation that captures the semantic meaning of words based on their context in a high-dimensional space. In machine learning and natural language processing, embeddings transform discrete categorical variables (like words) into continuous vectors of fixed dimensions. These vectors capture semantic relationships between words, meaning that words with similar meanings tend to have vectors that are close to each other in this space.
